Degree Type


Date of Award


Degree Name

Master of Science


Electrical and Computer Engineering

First Advisor

Lalita Udpa


Eddy current techniques are widely used to detect and characterize the defects in steam generator tubes in nuclear power plants. Although defect characterization is crucial for the successful inspection of defects, it is often difficult due to due to the finite size of the probes used for inspection. A feasible solution is to model the defect data as the convolution of the defect surface profile and the probe response. Therefore deconvolution algorithms can be used to remove the effect of probe on the signal.

This thesis presents a method using iterative blind deconvolution algorithm based on the Richardson-Lucy algorithm to address the defect characterization problem. Another iterative blind deconvolution method based on Wiener filtering is used to compare the performance. A preprocessing algorithm is introduced to remove the noise and thus enhance the performance. Two new convergence criterions are proposed to solve the convergence problem. Different types of initial estimate of the PSF are used and their impacts on the performance are compared. The results of applying this method to the synthetic data, the calibration data and the field data are presented.



Digital Repository @ Iowa State University,

Copyright Owner

Xiang Cai



Date Available

June 10, 2013

File Format


File Size

122 pages